Catching Ariadne by her thread: how a parasitoid exploits the herbivore's marking trails to locate its host
Bibliographic record
Abstract
Abstract Chemical signals that can be associated with the presence of a host insect often work as arrestants in close range host location by parasitoids, leading to longer searching times on patches where such signals are present. Our current view of parasitoid host location is that by prolonging the search times in patches, randomly searching parasitoids enhance their chance of detecting host insects. However, prolonged search times are not necessarily the only modification in parasitoid behaviour. In this study, we examine the exploitation of host‐fruit marking pheromone of rose‐hip flies, Rhagoletis basiola Osten‐Sacken (Diptera: Tephritidae) by the specialized egg‐larval parasitoid Halticoptera rosae Burks (Hymenoptera: Pteromalidae). We provide evidence that the instantaneous probability that a host egg will be located by a searching parasitoid wasp differs markedly between pheromone‐marked and unmarked fruits. The arresting response to the marking pheromone, i.e., the prolonged time a wasp is willing to search on marked fruits, can only account for a small fraction of the difference in successful host location on marked and unmarked fruits. We further demonstrate that the time wasps require to locate the host egg is independent of the size of the rose‐hip harbouring the fly egg, and thus is independent of the area the wasp potentially has to search. A comparison of our findings with results of different search algorithms for parasitoid wasps suggests that wasps use the fly's pheromone marking trail as a guide way to the fly's oviposition site and thus the host egg.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".